Do large language models (LLMs) provide gender-differentiated financial advice? We query 35 widely used LLMs from five vendors, varying only a single word in otherwise identical prompts: “man” versus “woman.” We find that women are advised to allocate 1.7 percentage points less to equity funds; this gap persists across vendors and model generations. Providing richer investor information, especially about risk preferences, attenuates but does not eliminate the gap, suggesting that gender partly serves as a proxy for unobserved characteristics. Since even modest allocation differences imply persistent return differentials, algorithmic financial advice can shape wealth accumulation across demographic groups.

A growing body of evidence suggests that lifetime experiences play an important role in determining households' investment choices. I estimate how such experiences shape individuals' beliefs about stock returns using a long-running panel data set of elicited beliefs. I then incorporate these findings into a life cycle model and examine the extent to which they can help resolve long-standing puzzles in the literature regarding stock market participation and the fraction of financial wealth invested in risky assets. I show that experience-based learning about returns creates a positive correlation between a household's position in the wealth distribution and its optimism about future returns. The wealthy consequently increase their investment in risky assets, while participation is limited among poorer households. I find that in a reasonably calibrated quantitative model, this mechanism is able to generate limited participation and an average risky share that is close to the data.

Using biennial data from the Health and Retirement Study, we estimate age-dependent health dynamics and survival probabilities at annual frequency conditional on race, sex, self-reported health and other covariates. The estimates can be used to calculate heterogeneous life expectancies in the population. We show that the racial life expectancy gap remains large, even conditional on health, socioeconomic and marital status. Due to racial differences in health dynamics and mortality, married black men on average can expect to receive $6,900 (or 9.5%) less in Social Security benefits in present value terms. Using a rich life cycle model, we estimate that this corresponds to a welfare loss of about 4%. Black married women's welfare loss is primarily driven not by their own shorter life expectancy but the shorter life expectancy of their husbands.

This paper examines how objective and subjective heterogeneity in life expectancy affects savings behavior of healthy and unhealthy people. Using data from the Health and Retirement Study, we first document systematic biases in survival beliefs across self-reported health: those in poor health not only have a shorter actual lifespan but also underestimate their remaining life time. To gauge the effect on savings behavior and wealth accumulation, we use an overlapping-generations model where survival probabilities and beliefs evolve according to a health and survival process estimated from data. We conclude that differences in life expectancy are important to understand savings behavior, and that the belief biases, especially among the unhealthy, can explain up to a fifth of the observed health-wealth gap.